What a dedicated team is

A dedicated team is a group of engineers a shop assembles for you and keeps working on your product over a long horizon. The shop usually manages the team day to day. You set priorities. They handle hiring, backfills, and internal process.

For AI work, the team often mixes roles: an engineer who owns the model layer, one on data pipelines, one on the application, and someone who runs evaluation. Together they cover what a single hire can't.

You can browse shops that offer this on the dedicated team engagement model page.

When it fits

A dedicated team makes sense when the work has no clean end date. Examples:

  • A platform that will grow for years, with new models and features each quarter.
  • A product where the model behavior needs constant tuning against real usage.
  • A company that needs AI capacity but has no engineering leader to run contractors one by one.

It fits poorly when you only need a prototype. For that, read the project-based AI development guide. If you already have a strong team and need a few specific skills, read the AI staff augmentation guide.

How it differs from the alternatives

  • Project-based: fixed scope, defined end. The shop owns delivery of that scope.
  • Staff augmentation: individuals join your team and follow your process. You manage them.
  • Dedicated team: a shop-managed unit that stays with you. Ownership of process sits with the shop. Direction sits with you.

The middle ground causes confusion. Ask each shop plainly: who manages the work, and who answers when something slips?

What to confirm before you start

Who is on the team. Get names and roles, not a role mix. Ask which of them also appear on the sales call.

How changes are handled. People leave. Ask how the shop replaces someone and how knowledge is handed over. Ask what happens to your context when it does.

How work is made visible. You should see a board, regular demos, and working software on a set cadence. If you only get status reports, push for more.

Evaluation ownership. In AI work, "does it work" is a measurement question. Agree early on who builds the test sets, who runs them, and how results reach you.

Code and data ownership. Your repository, your cloud accounts, your data. Confirm access from the first week so you are never locked out of your own system.

Exit terms. Ask how a wind-down works and what documentation you get. A good shop answers without discomfort.

Time zones. Overlap hours shape how fast questions get resolved. Use the region filters to narrow the list to shops whose working hours match yours.

Running it well

Treat the team as an extension of your product group, not a vendor you check on monthly. Give them a single point of contact on your side. Share business context, not only tickets. Review the roadmap together every quarter and adjust team composition when the work changes, for example moving from prototype-heavy work to evaluation and monitoring.

Start with a short, paid trial period on a real slice of work. It tells you more than a proposal does. The how to choose an AI dev shop guide covers how to structure that pilot.

Checklist

  • [ ] The work is ongoing, not a single deliverable.
  • [ ] You have named the person on your side who directs the team.
  • [ ] You know the exact engineers and roles assigned.
  • [ ] Backfill and handover process is described in writing.
  • [ ] Evaluation ownership is agreed.
  • [ ] Repos, cloud accounts, and data stay under your control.
  • [ ] Exit and wind-down terms are clear.
  • [ ] You compared at least three shops on the dedicated team page.